Supplemental Nutrition Assistance Program and Adherence to Antihypertensive Medications

Key Points Question Is receipt of Supplemental Nutrition Assistance Program (SNAP) benefits associated with nonadherence to antihypertensive medications and, if so, does this association vary by food insecurity status? Findings In this cohort study with 6692 participants, receipt of SNAP benefits was associated with a nearly 14–percentage point reduction in nonadherence to antihypertensive medications among food-insecure patients with hypertension but not among their food-secure counterparts. Meaning These findings suggest that SNAP should be further investigated as a potential intervention for preventing nonadherence to antihypertensive medications, especially among food-insecure individuals.


Introduction
Nearly half of US adults (approximately 121.5 million [47.3%]) have hypertension, which contributes to about 1000 deaths daily. 1,2Hypertension is a strong modifiable risk factor for cardiovascular diseases (CVDs), including coronary heart disease and stroke, which are the 2 leading causes of death in the US. 3,4The economic burden of hypertension is substantial for patients, health care systems, and countries, due to the loss of productivity from morbidity and premature death. 5,6According to the American Heart Association, annual health care costs (both direct and indirect) for the US population with hypertension are between $131 and $198 billion. 7Pharmacologic treatment with antihypertensive medications is the main approach to managing hypertension when nonpharmacologic interventions, such as physical activity, consumption of a healthy diet, and alcohol reduction, are unable to control hypertension in a timely fashion. 8,9[16] Although barriers to antihypertensive medication adherence remain multifactorial and complex, modifiable health-related social needs, such as food insecurity, are emerging as potential modifiable targets for improving adherence.8][19][20] Often, it is patients of limited financial means who must make this difficult choice.
The Supplemental Nutrition Assistance Program (SNAP) is the largest social intervention program in the US that provides financial assistance to low-income families through vouchers that can only be applied toward purchasing food. 21Some analyses have reported that SNAP participation may potentially reduce poverty by as much as 16%-equivalent to 8 million people-in the US. 22,23 addition, SNAP is an effective intervention for reducing food insecurity, as reported in a national study in which SNAP benefits were associated with an up to 30% reduction in food insecurity. 24ven that SNAP intervention is effective in reducing both poverty and food insecurity, which are both major risk factors for medication nonadherence, it is plausible that SNAP may be effective in preventing nonadherence to antihypertensive medications.In a recent analysis of patients with diabetes, researchers found that SNAP benefit receipt was associated with lower rates of costrelated medication nonadherence. 17In this analysis, we evaluated the association between SNAP benefit receipt and nonadherence to antihypertensive medications, and we further investigated the role of food insecurity in this association.

Methods
This cohort study was approved by The Ohio State University Institutional Review Board.Informed consent was waived because publicly available data were used.The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Conceptual Framework
The associations among poverty, food insecurity, nonadherence, and SNAP are highlighted through a conceptual framework in Figure 1.In this framework, we hypothesized that poverty may cause nonadherence to antihypertensive medications either directly or indirectly via food insecurity.Given that SNAP is an effective intervention for reducing both poverty and food insecurity, SNAP may potentially block the direct and indirect pathways through which poverty may lead to nonadherence as shown in Figure 1.

Data Source
For this analysis, we linked the household component of data from the Medical Expenditure Panel Survey (MEPS) for 2016 to 2017 25 and the National Health Interview Survey (NHIS) dataset. 26nkage between the MEPS and the NHIS was performed by the MEPS investigators prior to sharing the linked MEPS-NHIS files.These files also included a crosswalk, which we used to merge the MEPS full-year public use files and the NHIS person-level public use data files. 27At the time of completing the current analysis in 2020, the MEPS data files were updated up to 2018; within these data files, only the 2016 to 2017 files included food insecurity data.The MEPS-health care data are collected from a nationally representative sample of households through an overlapping panel design (eAppendix 1 in Supplement 1); these data are collected in 5 rounds of interviews during a 2-year period.Survey questionnaires are used to gather data regarding several factors on health care utilization and expenditure, including but not limited to self-reported health status, medical conditions, health insurance status, health care access, and prescription medication use.Similar to the MEPS, the NHIS is a nationally representative cross-sectional survey of US households that aims to collect health information including, but not limited, to SNAP participation status, demographic, socioeconomic factors, health status, health care access, and health behavior.

Study Design
We applied a retrospective cohort study design to assemble a cohort of antihypertensive medication users from the linked MEPS-NHIS dataset (eAppendix 1 in Supplement 1).We linked the MEPS and NHIS datasets for the analyses because although information on medication refill adherence (MRA), food insecurity, and several other important covariates of interest was captured in the 2016 to 2017 MEPS datasets, information regarding SNAP receipt status was assessed in only the NHIS data.All baseline covariates and effect modifiers (food insecurity status) were measured using data captured during the round 1 surveys from each data cycle, 2016 and 2017, respectively.For each survey year, participants were asked to report whether they had received SNAP any time during the prior year (eg, responses on SNAP receipt status in 2016 were in reference to whether a participant had received SNAP in 2015).The eFigure in Supplement 1 presents the initial sample of participants, those excluded because of restrictions, and the final sample used for analysis.

Outcome
The following detailed information on prescription medications was obtained directly from pharmacies after MEPS investigators had obtained consent from respondents: payments, payers, date each prescription was filled, quantity dispensed, and the National Drug Code of dispensed medications.The MEPS investigators also obtained from pharmacies information regarding the number of times that medications were filled by a MEPS participant within a given calendar year. 28 identified antihypertensive medication users through the therapeutic class codes corresponding to any of the antihypertensive agents listed in the eTable in Supplement 1.Based on information regarding the frequency and quantity of antihypertensive medication dispensed, we calculated MRA for antihypertensive medication use based on the following formula, which is widely used for measuring refill adherence [29][30][31] : MRA = (Sum of days' supply with antihypertensive medications) (No. of days [365] in study observation period) × 100% The mean number of MRAs per antihypertensive medication therapeutic class was calculated for patients who used multiple antihypertensive medications.If a patient's MRA was less than 80%, they were classified as nonadherent to antihypertensive medications.

Assessment of SNAP Status
In the NHIS, SNAP receipt status was assessed through a single question: "Did you or anyone in your family get benefits from SNAP in the past 12 months?"Individuals who responded positively to this question were classified as SNAP recipients, whereas those who did not were classified as nonrecipients.

Assessment of Food Security Status
3][34] For example, respondents were asked "how often in the last 30 days anyone in the household worried whether food would run out before getting money to buy more" or "how often in the last 30 the food purchased didn't last and the person/household didn't have money to get more." Responses to these questions were summed to create a score ranging from 0 to 10 for each MEPS participant.Individuals with scores of 1 or greater were considered food insecure, and those who responded negatively to all those items were considered food secure, which is the standard definition of food insecurity and food security based on the USDA Food Security Survey. 34,35

Potential Confounders
We applied the WHO multidimensional framework of medication adherence to guide the selection and inclusion of several covariates as potential confounders. 13Several individual patient, health care practitioner, and health care system factors are identified in the WHO framework as determinants of medication adherence.We applied a directed acyclic graph (Figure 2) to identify and visualize the hypothesized relationships between SNAP, food insecurity, and nonadherence to antihypertensive medications.Factors considered to be associated with both food insecurity and nonadherence to antihypertensive medications were selected as potential confounders and included several  1).Race and ethnicity was considered a confounder because Black individuals are significantly more likely to experience food insecurity, receive SNAP benefits, and be nonadherent to antihypertensive medications; additionally, race and ethnicity has been shown to be correlated with poverty, which is a determinant of both SNAP and nonadherence to antihypertensive medications.We created dummy variables for each of the baseline categorical variables for the analyses.

Statistical Analysis
Our analysis focused on addressing potential confounders and estimating the treatment effects of SNAP on medication nonadherence.First, inverse probability weighting techniques were used to address the potential confounding effects of the covariates identified through the directed acyclic graph approach.Propensity scores for each study participant were calculated as the probability of a patient receiving (vs not receiving) SNAP benefits, conditional on all measured baseline covariates; food insecurity was excluded from this model given that we prespecified this variable as an effect modifier.The resultant probabilities were inverted to create inverse probability weights (IPWs), which were then used for weighting the sample to control for measured confounding (eAppendix 2 in Supplement 1).After weighting the sample with IPWs, we applied a standardized difference test to assess the balance of the distribution of baseline covariates between SNAP recipients vs nonrecipients among the inverse probability weighted sample and the original unweighted sample.
Second, we estimated the population average treatment effects (PATEs) of SNAP on nonadherence to antihypertensive medications through a probit regression model that was weighted by the product of the IPW, and the MEPS survey weights, in the overall sample.The PATE in this overall sample represents the percentage point difference in nonadherence between SNAP recipients vs nonrecipients irrespective of participant food insecurity status.We tested whether the association between SNAP receipt status and nonadherence differed between the food-secure and food-insecure subgroups through a prespecified stratified analysis approach. 36To do this, we created subgroup-specific propensity scores separately for the foodsecure and food-insecure subgroups and we used these scores for weighting to control for the confounding effects of all baseline variables included in the overall propensity score model.Similar to the overall analysis, we next estimated the PATE for each food insecurity subgroup to determine whether food insecurity status modified the associations between SNAP and medication nonadherence.The potential effect modification of food insecurity was tested by evaluating for statistical differences in the magnitude and direction of the PATEs for the food-secure and foodinsecure subgroups; PATEs were considered statistically significantly different if their respective 95% CIs did not overlap.In sensitivity analysis, we evaluated for a potential dose-response association by quantifying the associations between levels of frequency of SNAP benefit receipt and nonadherence to antihypertensive medications.Because SNAP benefits are disbursed on a quarterly basis based on eligibility, an individual who is continuously eligible throughout the year would receive SNAP benefits 4 times (ie, for all 4 quarters of the year).We categorized the frequency of SNAP benefit receipt (1-3, 4-6, 7-9, and 10-12 times per year) and estimated PATEs, overall and by food insecurity status, for each of these categories.The category-specific PATEs were calculated by comparing the rate of nonadherence among each category of SNAP benefit receipt with nonreceipt (0 times per year).
We used Stata, version 14.0 (StataCorp LLC), to implement the statistical analyses as described in this section.Data analysis was performed from March to December 2021.

Potential Dose-Response Associations Between Frequency of SNAP Receipt and Medication Nonadherence
We observed 2 slightly contrasting U-shaped associations for the food-insecure subgroup and for the food-secure subgroup and the overall population.Although nonadherence was substantially lower among those who received SNAP 1 to 3 times per year, compared with nonrecipients, among the overall population, no associations were found among the food-insecure and food-secure subgroups.
However, the direction of the association reversed when comparing receipt of SNAP 4 to 6 times per year vs no receipt; this frequency of SNAP receipt was associated with substantially higher nonadherence for the overall population and the food-secure subgroup but not for the food-insecure subgroup.A similar form of association was observed for the third-highest level of SNAP receipt.

Discussion
Among a US sample of patients with hypertension identified from a linked, nationally representative dataset, we observed that SNAP had a differential association with nonadherence to antihypertensive medications between food-secure and food-insecure subgroups.Specifically, individuals who received SNAP had a lower rate of nonadherence to antihypertensive medications among the food-insecure subgroup but not among the food-secure subgroup or the overall population.These findings suggest that SNAP may potentially mitigate against the risk of nonadherence to antihypertensive medications due to food insecurity.Our study contributes to the growing literature on how existing social intervention programs such as SNAP may be leveraged to prevent negative consequences of health-related social needs on health outcomes, including medication adherence.
To our knowledge, no published studies have specifically evaluated the role of food insecurity as a potential effect modifier of the associations between SNAP and adherence to antihypertensive medications among a nationally representative sample of insured and uninsured adults in the US.
However, a few published studies have evaluated the overall associations between SNAP and

Figure 2 .
Figure 2. Directed Acyclic Graph for Identifying Confounders and Effect Modifiers of the Associations Between Supplemental Nutrition Assistance Program (SNAP) Benefit Receipt and Medication Nonadherence

Figure 3 .
Figure 3.Estimated Population Average Treatment Effects (PATEs) of the Associations Between Levels of Frequency of Supplemental Nutrition Assistance Program (SNAP) Benefit Receipt and Nonadherence to Antihypertensive Medications 70 50 30 10 -10 0 American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, unknown race or ethnicity, and other or multiple races or ethnicities]), socioeconomic status (eg, educational attainment and poverty level), health status (eg, poor physical health and functional limitation), and health care access (eg, health insurance and out-of-pocket costs) (Table demographic factors, such as age, sex, self-reported race and ethnicity (Hispanic, non-Hispanic Black [hereinafter, Black], non-Hispanic White [hereinafter, White], and other race or ethnicity [including

Table 1 .
Distribution of Baseline Covariates by SNAP Status Before and After Inverse Probability Weighting b Encompasses individuals who identified as American Indian or Alaska Native, Asian, or Native Hawaiian or Other Pacific Islander; declined to disclose race and ethnicity; or identified as other or multiple races or ethnicities.cNot included in the propensity score model.
total of 1602 participants (15.2%) were Black, 1175 (9.8%) were Hispanic, 3711 (71.6%) were White, and 204 (3.4%) were of other race or ethnicity.There were 1203 participants (12.8%; surveyweighted n = 8 553 051) who had received SNAP benefits.A summary of the distribution of baseline covariates, overall and by SNAP status, is reported in Table1.Overall, 1338 individuals (14.8%) reported experiencing food insecurity in the past 30 days prior to being surveyed; however, nearly half of the SNAP recipients (540 [42.9%]) reported being food insecure compared with nonrecipients (798 [10.8%]).After the sample was weighted by IPWs, all baseline covariates were balanced between SNAP recipients and nonrecipients.

Table 2 .
Estimated Percentage Point Difference in Nonadherence to Antihypertensive Medications by Overall Population and Food Security Status ] vs 491 [60.0%weighted]; PATE, −13.6 [95% CI, −25.0 to −2.3]).The observed PATE estimates among the food-secure vs food-insecure subgroups were statistically significantly different, suggesting that food insecurity modified the association between SNAP and nonadherence to antihypertensive medication therapy.
a Values are presented as No. (%) of participants, with unweighted No. values and weighted percentages given.bEstimatedPATEvalues are the average marginal effects from a logit model and show the percentage point difference in nonadherence to antihypertensive medications between SNAP recipients (vs non-SNAP recipients) with hypertension.All regressions include a constant and all other covariates from Table1and are weighted by a product of the propensity score weight and the survey weight.weighted